Background While the concept of medication reconciliation seems relatively straightforward, implementing medication reconciliation has proved to be complex and challenging. In our setting, a teaching hospital with 700 beds, it seems very hard to perform extensive and complete reconciliation for every patient.
Purpose The objectives of this study were to describe the frequency and type of medication discrepancies (MD) during admission in cardiology, and to identify patients with a high risk of unintended medication discrepancies (UMD).
Material and methods Medication reconciliation was conducted at admission in the cardiology department over 4 weeks by trained pharmacists. (1) The best possible medication history (BPMH) was obtained using multiple sources (interview with the patient/family member, prescription vials, medication list, contact with general practitioner and community pharmacy, medical and pharmaceutical files). (2) Comparison of BPMH with the initial hospital prescription, identification of MD. (3) Classification of MD (intended/unintended) with the physician. Tools have been tested and validated in a pilot study. Statistical analysis examined the associations between UMD and patient reported factors (performed using R software). Statistical significance was reached if p<0.05.
Results During the study period, 100 patients were included, mean age 67.6 years (SD 17.7), sex ratio (M/F) 1.3, corresponding to 746 prescription lines. Overall, 544 MD were identified, including 77 UMD (42% of patients). The most common UMD was omission (70%). Do not speak French (p=0.007), to be admitted to a hospitalisation unit (compared with intensive care unit) (p=0.019), a low level of education (p=0.004), ≥2 comorbidities (p=0.001), long term illness (p=0.042) and ≥8 drugs in the initial prescription (p=0.004) were found to be significantly associated with UMD. Level of education remained significantly and independently associated with UMD after adjusting in the multivariate analysis for factors statistically significant in the univariate analysis.
Conclusion Our study allowed us to identify predicting factors for UMD. Selection of patients for medication reconciliation must take into consideration factors that have been statistically identified, but are also practical. It is difficult to obtain information about level of education. Therefore, we decided to prioritise patients with ≥ 8 drugs in the initial prescription for medication reconciliation.
No conflict of interest
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